What Is AI as a Service (AIaaS)? Definition, Models, and Evaluation

AI as a Service (AIaaS) is a delivery model where AI capabilities are provided via a managed platform or provider. Instead of hiring an ML team to train and operate models, you consume AI through APIs or managed workflows.

AIaaS can mean different things in the market, so we use a concrete definition: AIaaS includes (1) the model access, (2) the infrastructure to run it, (3) monitoring and evaluation, and (4) operational support. If you only get an API key and no governance story, you’re buying raw model access—not a service.

For agentic use cases, AIaaS often means a managed agent: the provider operates the orchestration, tool integrations, and guardrails.

Typical AIaaS building blocks

A real AIaaS offering usually contains more than a model endpoint:

• Model access (LLM or specialised models)


• Prompting / templates and versioning


• Retrieval or connectors to your data sources


• Evaluation (regression cases, sampling, measurable metrics)


• Observability (logs, traces, cost dashboards)


• Security controls (scoped tokens, audit logs, encryption)


• Operational support (incident response and improvements)

“Managed” means someone owns reliability

AI systems drift: tools change, policies change, and the business changes. A managed AIaaS provider should be able to tell you how they handle:

• Versioning prompts and tool schemas


• Regression testing when something changes


• Incident response when an automation misbehaves


• Cost controls and usage monitoring

AIaaS for agents vs AIaaS for analytics

Some AIaaS products are about analytics (predict, score, classify). Others are about operations (take actions). The operational category is where governance matters most, because the AI output can change customer accounts, financial records, or workflows.

AIaaS overlaps with SaaS and PaaS, but the value proposition is different.

The biggest practical difference is where the complexity lives. With SaaS you adopt an app. With PaaS you build. With AIaaS you usually integrate and govern: you connect the AI capability to your data and workflows and you need controls for quality and compliance.

In Dutch teams, we often see AIaaS used to augment existing SaaS: an agent reads Zendesk tickets, fetches context from Shopify/Exact, and drafts actions—without replacing the core systems.

Who owns what (responsibility clarity)

A useful way to compare these models is by responsibility:

• With SaaS, the vendor owns the app logic, uptime, and security posture; you configure it.


• With PaaS, you own the application and the policies; the vendor owns the infrastructure primitives.


• With AIaaS, responsibility is shared: the provider owns model operations, but you still own policy, data selection, and final accountability for business actions.

Governance layer (why AIaaS is different)

With SaaS, the vendor’s UI is the product. With AIaaS, the product is a behaviour that runs inside your workflow. That means you need governance artifacts: evaluation results, audit logs, and a clear policy for what happens when the AI is uncertain.

If you’re automating business actions (refunds, bookings, account updates), you should treat AIaaS like a critical integration, not like a plug-in widget.

Practical selection rule

If you want a finished workflow with minimal engineering, you’re leaning toward SaaS. If you want maximum flexibility and you have engineers, PaaS works. If you want AI capability embedded inside existing systems—with governance and ongoing improvement—AIaaS is usually the right model.

AIaaS can be deployed in several ways. The right choice depends on data sensitivity, latency, and regulatory constraints.

1) Cloud-managed

The provider runs the models and orchestration in their cloud. Fastest to start, but you must be comfortable with their data handling and sub-processors.

2) Hybrid

Some components run in your environment (data connectors, sensitive processing) while model calls run in a managed environment. This is common when teams want tighter control over data residency.

3) On-premise / self-hosted

Models and orchestration run inside your infrastructure. This can satisfy strict policies, but increases operational burden.

4) Air-gapped

Used in high-security environments. It’s feasible, but it changes the economics and the update cycle.

EU data residency and private connectivity

If data residency matters, ask two separate questions: where is data processed, and where is it stored? Some providers can run inference in an EU region but still log prompts elsewhere. You want clarity on both.

For sensitive workflows, hybrid deployments can be a good compromise: connectors run inside your environment, the provider processes only minimal fields, and you keep a clear audit trail. For highly regulated environments, dedicated environments (VPC / private endpoints) reduce exposure.

Latency and availability

Deployment choice also affects performance. Cloud-managed setups can be fast, but latency depends on network and model runtime. Hybrid and on-prem can reduce data exposure but may increase operational load.

For customer-facing workflows, we often design for graceful degradation: if the AI service is unavailable, the system falls back to templates and routes to a human instead of blocking the business.

AIaaS pricing is often a mix of fixed and variable cost. Common models include:

• Per-user: predictable for internal copilots, less aligned for high-volume automation.


• Per-transaction / per-document: common for invoice extraction, ticket handling, or classification.


• Usage-based: cost tied to tokens, calls, or compute.


• Flat-rate + limits: simple packaging for MKB.


• Custom enterprise: governance, SLAs, and dedicated environments.

Cost example (make it tangible)

Suppose you automate support triage for 10,000 tickets/month. If the marginal AI cost is €0.20 per ticket, that’s €2,000/month in usage. If the agent reduces human handling time by even 2 minutes per ticket for 30% of tickets, that’s 10,000 × 30% × 2 minutes = 6,000 minutes (100 hours) saved per month. At €50/hour fully loaded, that’s €5,000/month of capacity.

The point: you can only evaluate pricing when you connect it to the workflow and your internal cost.

Cost controls (what separates pilots from production)

AI usage costs can creep if you don’t design for it. We look for controls like:

• Caching for repeated questions


• Batching low-urgency work


• Hard caps and alerts


• Token/usage budgeting per workflow

Hidden costs to account for

• Human exception review time (early weeks are higher)


• Integration maintenance when APIs change


• Model/provider changes that require re-testing

For European businesses, AIaaS is attractive for practical reasons:

• No ML team required: you can deploy useful automation without hiring specialists.


• Fast time-to-value: you can pilot in weeks, not quarters.


• Scalability: the same system can handle 100 or 10,000 cases if integrations are solid.


• Compliance posture: good AIaaS providers can offer DPAs, data residency options, and audit logs.

Compliance as part of the service

In the EU, the fastest deployments are the ones where compliance is built into the product. That means: DPAs, documented sub-processors, clear retention, and the ability to support data subject rights without manual chaos.

When AIaaS is done well, you get both speed and control: you can pilot quickly, then add approvals and tighter permissions as you expand.

Operational benefits

AIaaS can also improve consistency. Humans get tired and policies get applied unevenly. A well-governed AI workflow applies the same checklist every time and escalates the same exception types. That consistency matters in finance and customer service where small errors create big follow-up work.

European advantage: standardisation

European rules can feel heavy, but they create a benefit: once you have a compliant pattern (DPA, logging, minimised data), you can reuse it across workflows. AIaaS makes that reuse easier because the governance layer is part of the service.

If AIaaS touches personal data, GDPR/AVG is unavoidable. The most important questions are operational, not theoretical:

• What data is processed, for what purpose, and for how long?


• Where is the data stored and where is it processed (EU vs non-EU)?


• Who are the sub-processors and what security measures are in place?


• Can you support data subject rights (access, deletion) without chaos?

Controller vs processor (keep roles clear)

In most AIaaS setups, your business remains the data controller and the provider acts as a processor. That means you need a DPA, and you remain accountable for lawful basis, purpose limitation, and minimisation.

Also plan for operational GDPR tasks:

• DSAR handling: data access/deletion requests typically require a response within one month (with limited extension options).


• Prompt retention: how long do logs exist, and can you delete them?


• Sub-processor changes: how are you notified, and can you object?

Data minimisation and purpose limitation

A GDPR-conscious AIaaS design fetches only what is needed for the next step. For example: for order tracking, you need order status and carrier events—not the customer’s full profile. This reduces risk and makes compliance easier to explain.

If the workflow is high risk, consider a DPIA and keep a clear record of processing activities.

Cross-border transfers and sub-processors

If a provider processes or stores data outside the EU/EEA, you need to understand the transfer mechanism (for example: Standard Contractual Clauses) and the provider’s sub-processor chain. Even if you don’t run a legal deep dive, you should at least know where data flows and what you can control.

For many Dutch businesses, the simplest requirement is also the strongest: EU-based processing for sensitive workflows, plus audit logs and strict retention for prompts.

We use an 8-point framework when we evaluate AIaaS providers for Dutch businesses:

1) Data residency and processing: can you keep data in the EU if required?
2) Security: encryption, access controls, logging, incident response.
3) Tool integrations: can it connect to the systems where work happens?
4) Guardrails: permissions, approvals, and refusal behaviour.
5) Evaluation: regression tests, sampling, measurable metrics.
6) Observability: audit trails per case, tool-call logs, dashboards.
7) Commercial model: pricing clarity and predictable scaling.
8) Support and change management: how quickly do issues get resolved and improvements shipped?

If a provider can’t answer these clearly, you’ll end up doing the hard work yourself.

Red flags (we see these often)

• The provider cannot show an audit log of tool calls and data access.


• The provider cannot explain where prompts are stored and for how long.


• “Automation” is promised without an exception workflow or approvals.


• Pricing is opaque and cost controls are missing.

Request these artifacts before signing

We ask providers for concrete artifacts, not promises:

• A sample audit log (tool calls + decisions)


• Their DPA and sub-processor list


• An evaluation report or at least a described evaluation process


• A retention policy (what is stored, where, for how long)


• A security overview (access controls, encryption, incident process)

Scoring approach (quick and useful)

We often score providers 1–5 on each of the 8 points and require a minimum threshold on governance (security, guardrails, evaluation). A provider that scores high on model quality but low on governance creates operational risk.

If you want one question per point:

• Data residency: where is processing and storage?


• Security: what are the access controls and logs?


• Integrations: can it fetch and verify context?


• Guardrails: can it refuse and escalate correctly?


• Evaluation: how do changes get regression-tested?


• Observability: can we trace a decision end-to-end?


• Commercials: can we predict cost at 2× volume?


• Support: how are incidents handled and fixed?

We deliver AI agents as a managed service for Dutch businesses. That means we don’t just ship prompts. We ship a system: integrations, guardrails, logging, and an exception workflow so humans stay in control.

Practically, our approach is:

• Start with one narrow workflow (bookkeeping categorisation, support triage).


• Run in read-only/draft mode, measure outcomes, and tighten policies.


• Expand to execution for reversible actions, and keep approvals for high-risk steps.

What we operate

When we run managed agents, we operate the integration layer, the policy layer, and the evaluation loop. That includes regression cases, monitoring costs, and updating prompts/tool schemas as your business changes.

A typical first delivery is 2–4 weeks for a narrow workflow with measurable outcomes. After that, expansion is usually faster because the orchestration and logging foundations are already in place.

Our delivery loop

We run managed agents as an iterative loop: ship a narrow workflow, measure outcomes, review the top exceptions, then tighten guardrails and expand. We prefer steady improvement over a big-bang automation rollout.

For Dutch clients, we default to EU-conscious processing, scoped tool permissions, and an explicit exception queue so humans can stay accountable.

We default to EU-conscious design: scoped tool access, encryption, and clear logging. For banking and bookkeeping workflows, we keep access read-only where possible and treat submission steps as human actions with AI preparation.